Method and Apparatus for Providing Glycemic Control

ABSTRACT

Methods and system to provide glycemic control and therapy management based on monitored glucose data, and current and/or target Hb1AC levels are provided.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.12/476,107 filed Jun. 1, 2009, which claims priority under 35 USC§119(e) to U.S. Provisional application No. 61/057,789 filed May 30,2008, entitled “Method and Apparatus for Providing Glycemic Control”,and U.S. Provisional Application No. 61/097,504 filed Sep. 16, 2008,entitled “Therapy Management Based on Continuous Glucose Data and MealInformation”, the disclosures of each of which are incorporated hereinby reference for all purposes.

BACKGROUND

The detection of the level of analytes, such as glucose, lactate,oxygen, and the like, in certain individuals is vitally important totheir health. For example, the monitoring of glucose is particularlyimportant to individuals with diabetes. Diabetics may need to monitorglucose levels to determine when insulin is needed to reduce glucoselevels in their bodies or when additional glucose is needed to raise thelevel of glucose in their bodies.

Accordingly, of interest are devices that allow a user to test for oneor more analytes, and provide glycemic control and therapy management.

SUMMARY

Embodiments of the present disclosure also include method and apparatusfor receiving mean glucose value information of a patient based on apredetermined time period, receiving a current HbA1C level of thepatient and a target HbA1C level of the patient, determining acorrelation between the received mean glucose value information and theretrieved current and target HbA1C levels, updating the target HbA1Clevel based on the determined correlation, and determining one or moreparameters associated with the physiological condition of the patientbased on the updated target HbA1C level.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of an embodiment of a data monitoring andmanagement system according to the present disclosure;

FIG. 2 shows a block diagram of an embodiment of the transmitter unit ofthe data monitoring and management system of FIG. 1;

FIG. 3 shows a block diagram of an embodiment of the receiver/monitorunit of the data monitoring and management system of FIG. 1;

FIG. 4 shows a schematic diagram of an embodiment of an analyte sensoraccording to the present disclosure;

FIGS. 5A-5B show a perspective view and a cross sectional view,respectively of another embodiment an analyte sensor;

FIG. 6 provides a tabular illustration of the demographic andcharacteristics of participants in the 90 days continuous glucosemonitoring system study used in one aspect;

FIG. 7 is a chart illustrating the relationship between the mean 90 daycontinuously monitored glucose level and the mean 90 day discrete bloodglucose test results compared with the HbA1C level in one aspect;

FIG. 8 provides a graphical illustration of the individual rates ofglycation distribution in one aspect;

FIG. 9 provides a graphical illustration of the slope and correlation ofthe continuously monitored glucose level to the HbA1C level on a weeklybasis in one aspect;

FIG. 10 is a graphical illustration of the frequency of the obtainedglucose levels between the SMBG (self monitored blood glucose)measurements and the CGM (continuously monitored glucose) measurement ona daily basis in one aspect;

FIG. 11 is a graphical illustration of the glucose measurementdistribution by time of day between the SMBG (self monitored bloodglucose) measurements and the CGM (continuously monitored glucose)measurement in one aspect;

FIG. 12 is a tabular illustration of the study subject characteristicsby baseline HbA1C level in one aspect;

FIG. 13 is a graphical illustration of the increase in the number ofstudy subjects that achieved in-target HbA1C during the 90 day studyduration in one aspect;

FIG. 14 is a graphical illustration of the difference between the meanglucose level of subjects with in-target HbA1C level compared toabove-target HbA1C level during the study duration of 90 days in oneaspect;

FIG. 15 is a graphical illustration of the glucose variation betweensubjects with in-target

HbA1C level compared to above-target HbA1C level during the studyduration of 90 days in one aspect;

FIG. 16 is a graphical illustration of the average percentage HbA1Clevel change based on the number of times the study subjects viewed thecontinuously monitored glucose level in one aspect;

FIG. 17 graphically illustrates the weekly glycemic control resultsbased on the number of times daily the subjects viewed the real timecontinuously monitored glucose levels in one aspect;

FIG. 18 is a graphical illustration of the glycemic variability measuredas the standard deviation on a weekly basis of the subjects between thenumber of times daily the subjects viewed the real time continuouslymonitored glucose levels in one aspect;

FIG. 19 is a tabular illustration of three hypothetical subjects toevaluate and modify target continuously monitored glucose levels basedon HbA1C measurements, average 30 day CGM data, and percentage ofduration in hypoglycemic condition over the 30 day period in one aspect;

FIG. 20 illustrates routines for managing diabetic conditions based onHbA1C level and mean glucose data in one aspect;

FIG. 21 illustrates routines for managing diabetic conditions based onHbA1C level and mean glucose data in another aspect; and

FIG. 22 is a flowchart illustrating a therapy guidance routine based inpart on the HbA1C level in one aspect.

DETAILED DESCRIPTION

Before the present disclosure is described, it is to be understood thatthis disclosure is not limited to particular embodiments described, assuch may, of course, vary. It is also to be understood that theterminology used herein is for the purpose of describing particularembodiments only, and is not intended to be limiting, since the scope ofthe present disclosure will be limited only by the appended claims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the disclosure. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges as also encompassed within the disclosure, subject to anyspecifically excluded limit in the stated range. Where the stated rangeincludes one or both of the limits, ranges excluding either or both ofthose included limits are also included in the disclosure.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure.

The figures shown herein are not necessarily drawn to scale, with somecomponents and features being exaggerated for clarity.

Generally, embodiments of the present disclosure relate to methods anddevices for detecting at least one analyte such as glucose in bodyfluid. Embodiments relate to the continuous and/or automatic in vivomonitoring of the level of one or more analytes using a continuousanalyte monitoring system that includes an analyte sensor at least aportion of which is to be positioned beneath a skin surface of a userfor a period of time and/or the discrete monitoring of one or moreanalytes using an in vitro blood glucose (“BG”) meter and an analytetest strip. Embodiments include combined or combinable devices, systemsand methods and/or transferring data between an in vivo continuoussystem and a BG meter system.

Embodiments of the present disclosure include method and apparatus forreceiving mean glucose value information of a patient based on apredetermined time period, receiving an HbA1C (also referred to as A1C)level of the patient, determining a correlation between the receivedmean glucose value information and the HbA1C level, and determining atarget HbA1C level based on the determined correlation, for example, fordiabetes management or physiological therapy management. Additionally,in certain embodiments of the present disclosure there are providedmethod, apparatus, and system for receiving mean glucose valueinformation of a patient based on a predetermined time period, receivinga current HbA1C level of the patient and a target HbA1C level of thepatient, determining a correlation between the received mean glucosevalue information and the retrieved current and target HbA1C levels,updating the target HbA1C level based on the determined correlation, anddetermining one or more parameters associated with the physiologicalcondition of the patient based on the updated target HbA1C level.

Accordingly, embodiments include analyte monitoring devices and systemsthat include an analyte sensor—at least a portion of which ispositionable beneath the skin of the user—for the in vivo detection, ofan analyte, such as glucose, lactate, and the like, in a body fluid.Embodiments include wholly implantable analyte sensors and analytesensors in which only a portion of the sensor is positioned under theskin and a portion of the sensor resides above the skin, e.g., forcontact to a transmitter, receiver, transceiver, processor, etc. Thesensor may be, for example, subcutaneously positionable in a patient forthe continuous or periodic monitoring of a level of an analyte in apatient's interstitial fluid. For the purposes of this description,continuous monitoring and periodic monitoring will be usedinterchangeably, unless noted otherwise. The sensor response may becorrelated and/or converted to analyte levels in blood or other fluids.In certain embodiments, an analyte sensor may be positioned in contactwith interstitial fluid to detect the level of glucose, in whichdetected glucose may be used to infer the glucose level in the patient'sbloodstream. Analyte sensors may be insertable into a vein, artery, orother portion of the body containing fluid. Embodiments of the analytesensors of the subject disclosure may be configured for monitoring thelevel of the analyte over a time period which may range from minutes,hours, days, weeks, or longer.

Of interest are analyte sensors, such as glucose sensors, that arecapable of in vivo detection of an analyte for about one hour or more,e.g., about a few hours or more, e.g., about a few days of more, e.g.,about three or more days, e.g., about five days or more, e.g., aboutseven days or more, e.g., about several weeks or at least one month.Future analyte levels may be predicted based on information obtained,e.g., the current analyte level at time t0, the rate of change of theanalyte, etc. Predictive alarms may notify the user of a predictedanalyte level that may be of concern in advance of the user's analytelevel reaching the future level. This provides the user an opportunityto take corrective action.

FIG. 1 shows a data monitoring and management system such as, forexample, an analyte (e.g., glucose) monitoring system 100 in accordancewith certain embodiments. Embodiments of the subject disclosure arefurther described primarily with respect to glucose monitoring devicesand systems, and methods of glucose detection, for convenience only andsuch description is in no way intended to limit the scope of thedisclosure. It is to be understood that the analyte monitoring systemmay be configured to monitor a variety of analytes at the same time orat different times.

Analytes that may be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin,creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine,glucose, glutamine, growth hormones, hormones, ketone bodies, lactate,peroxide, prostate-specific antigen, prothrombin, RNA, thyroidstimulating hormone, and troponin. The concentration of drugs, such as,for example, antibiotics (e.g., gentamicin, vancomycin, and the like),digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may alsobe monitored. In those embodiments that monitor more than one analyte,the analytes may be monitored at the same or different times.

The analyte monitoring system 100 includes a sensor 101, a dataprocessing unit 102 connectable to the sensor 101, and a primaryreceiver unit 104 which is configured to communicate with the dataprocessing unit 102 via a communication link 103. In certainembodiments, the primary receiver unit 104 may be further configured totransmit data to a data processing terminal 105 to evaluate or otherwiseprocess or format data received by the primary receiver unit 104. Thedata processing terminal 105 may be configured to receive data directlyfrom the data processing unit 102 via a communication link which mayoptionally be configured for bi-directional communication. Further, thedata processing unit 102 may include a transmitter or a transceiver totransmit and/or receive data to and/or from the primary receiver unit104 and/or the data processing terminal 105 and/or optionally thesecondary receiver unit 106.

Also shown in FIG. 1 is an optional secondary receiver unit 106 which isoperatively coupled to the communication link and configured to receivedata transmitted from the data processing unit 102. The secondaryreceiver unit 106 may be configured to communicate with the primaryreceiver unit 104, as well as the data processing terminal 105. Thesecondary receiver unit 106 may be configured for bi-directionalwireless communication with each of the primary receiver unit 104 andthe data processing terminal 105. As discussed in further detail below,in certain embodiments the secondary receiver unit 106 may be ade-featured receiver as compared to the primary receiver unit, i.e., thesecondary receiver unit may include a limited or minimal number offunctions and features as compared with the primary receiver unit 104.As such, the secondary receiver unit 106 may include a smaller (in oneor more, including all, dimensions), compact housing or be embodied in adevice such as a wrist watch, arm band, etc., for example.Alternatively, the secondary receiver unit 106 may be configured withthe same or substantially similar functions and features as the primaryreceiver unit 104. The secondary receiver unit 106 may include a dockingportion to be mated with a docking cradle unit for placement by, e.g.,the bedside for night time monitoring, and/or a bi-directionalcommunication device. A docking cradle may recharge a power supply.

Only one sensor 101, data processing unit 102 and data processingterminal 105 are shown in the embodiment of the analyte monitoringsystem 100 illustrated in FIG. 1. However, it will be appreciated by oneof ordinary skill in the art that the analyte monitoring system 100 mayinclude more than one sensor 101 and/or more than one data processingunit 102, and/or more than one data processing terminal 105. Multiplesensors may be positioned in a patient for analyte monitoring at thesame or different times. In certain embodiments, analyte informationobtained by a first positioned sensor may be employed as a comparison toanalyte information obtained by a second sensor. This may be useful toconfirm or validate analyte information obtained from one or both of thesensors. Such redundancy may be useful if analyte information iscontemplated in critical therapy-related decisions. In certainembodiments, a first sensor may be used to calibrate a second sensor.

The analyte monitoring system 100 may be a continuous monitoring system,or semi-continuous, or a discrete monitoring system. In amulti-component environment, each component may be configured to beuniquely identified by one or more of the other components in the systemso that communication conflict may be readily resolved between thevarious components within the analyte monitoring system 100. Forexample, unique IDs, communication channels, and the like, may be used.

In certain embodiments, the sensor 101 is physically positioned in or onthe body of a user whose analyte level is being monitored. The sensor101 may be configured to at least periodically sample the analyte levelof the user and convert the sampled analyte level into a correspondingsignal for transmission by the data processing unit 102. The dataprocessing unit 102 is coupleable to the sensor 101 so that both devicesare positioned in or on the user's body, with at least a portion of theanalyte sensor 101 positioned transcutaneously. The data processing unit102 may include a fixation element such as adhesive or the like tosecure it to the user's body. A mount (not shown) attachable to the userand mateable with the data processing unit 102 may be used. For example,a mount may include an adhesive surface. The data processing unit 102performs data processing functions, where such functions may include,but are not limited to, filtering and encoding of data signals, each ofwhich corresponds to a sampled analyte level of the user, fortransmission to the primary receiver unit 104 via the communication link103. In one embodiment, the sensor 101 or the data processing unit 102or a combined sensor/data processing unit may be wholly implantableunder the skin layer of the user.

In certain embodiments, the primary receiver unit 104 may include ananalog interface section including an RF receiver and an antenna that isconfigured to communicate with the data processing unit 102 via thecommunication link 103, and a data processing section for processing thereceived data from the data processing unit 102 such as data decoding,error detection and correction, data clock generation, data bitrecovery, etc., or any combination thereof.

In operation, the primary receiver unit 104 in certain embodiments isconfigured to synchronize with the data processing unit 102 to uniquelyidentify the data processing unit 102, based on, for example, anidentification information of the data processing unit 102, andthereafter, to periodically receive signals transmitted from the dataprocessing unit 102 associated with the monitored analyte levelsdetected by the sensor 101.

Referring again to FIG. 1, the data processing terminal 105 may includea personal computer, a portable computer such as a laptop or a handhelddevice (e.g., personal digital assistants (PDAs), telephone such as acellular phone (e.g., a multimedia and Internet-enabled mobile phonesuch as an iPhone, Blackberry device, a Palm device or similar phone),mp3 player, pager, GPS (global positioning system) device and the like),or a drug delivery device, each of which may be configured for datacommunication with the receiver via a wired or a wireless connection.Additionally, the data processing terminal 105 may further be connectedto a data network (not shown) for storing, retrieving, updating, and/oranalyzing data corresponding to the detected analyte level of the user.

The data processing terminal 105 may include an infusion device such asan insulin infusion pump or the like, which may be configured toadminister insulin to patients, and which may be configured tocommunicate with the primary receiver unit 104 for receiving, amongothers, the measured analyte level. Alternatively, the primary receiverunit 104 may be configured to integrate an infusion device therein sothat the primary receiver unit 104 is configured to administer insulin(or other appropriate drug) therapy to patients, for example, foradministering and modifying basal profiles, as well as for determiningappropriate boluses for administration based on, among others, thedetected analyte levels received from the data processing unit 102. Aninfusion device may be an external device or an internal device (whollyimplantable in a user).

In certain embodiments, the data processing terminal 105, which mayinclude an insulin pump, may be configured to receive the analytesignals from the data processing unit 102, and thus, incorporate thefunctions of the primary receiver unit 104 including data processing formanaging the patient's insulin therapy and analyte monitoring. Incertain embodiments, the communication link 103 as well as one or moreof the other communication interfaces shown in FIG. 1, may use one ormore of an RF communication protocol, an infrared communicationprotocol, a Bluetooth® enabled communication protocol, an 802.11xwireless communication protocol, or an equivalent wireless communicationprotocol which would allow secure, wireless communication of severalunits (for example, per HIPAA requirements), while avoiding potentialdata collision and interference.

FIG. 2 shows a block diagram of an embodiment of a data processing unitof the data monitoring and detection system shown in FIG. 1. The dataprocessing unit 102 thus may include one or more of an analog interface201 configured to communicate with the sensor 101 (FIG. 1), a user input202, and a temperature measurement section 203, each of which isoperatively coupled to a processor 204 such as a central processing unit(CPU). User input and/or interface components may be included or a dataprocessing unit may be free of user input and/or interface components.In certain embodiments, one or more application-specific integratedcircuits (ASIC) may be used to implement one or more functions orroutines associated with the operations of the data processing unit(and/or receiver unit) using for example one or more state machines andbuffers.

Further shown in FIG. 2 are a serial communication section 205 and an RFtransmitter 206, each of which is also operatively coupled to theprocessor 204. The RF transmitter 206, in some embodiments, may beconfigured as an RF receiver or an RF transmitter/receiver, such as atransceiver, to transmit and/or receive data signals. Moreover, a powersupply 207, such as a battery, may also be provided in the dataprocessing unit 102 to provide the necessary power for the dataprocessing unit 102. Additionally, as can be seen from the Figure, clock208 may be provided to, among others, supply real time information tothe processor 204.

As can be seen in the embodiment of FIG. 2, the sensor unit 101 (FIG. 1)includes four contacts, three of which are electrodes—work electrode (W)210, reference electrode (R) 212, and counter electrode (C) 213, eachoperatively coupled to the analog interface 201 of the data processingunit 102. This embodiment also shows an optional guard contact (G) 211.Fewer or greater electrodes may be employed. For example, the counterand reference electrode functions may be served by a singlecounter/reference electrode, there may be more than one workingelectrode and/or reference electrode and/or counter electrode, etc.

In certain embodiments, a unidirectional input path is established fromthe sensor 101 (FIG. 1) and/or manufacturing and testing equipment tothe analog interface 201 of the data processing unit 102, while aunidirectional output is established from the output of the RFtransmitter 206 of the data processing unit 102 for transmission to theprimary receiver unit 104. In this manner, a data path is shown in FIG.2 between the aforementioned unidirectional input and output via adedicated link 209 from the analog interface 201 to serial communicationsection 205, thereafter to the processor 204, and then to the RFtransmitter 206. As such, in certain embodiments, via the data pathdescribed above, the data processing unit 102 is configured to transmitto the primary receiver unit 104 (FIG. 1), via the communication link103 (FIG. 1), processed and encoded data signals received from thesensor 101 (FIG. 1). Additionally, the unidirectional communication datapath between the analog interface 201 and the RF transmitter 206discussed above allows for the configuration of the data processing unit102 for operation upon completion of the manufacturing process as wellas for direct communication for diagnostic and testing purposes.

The processor 204 may be configured to transmit control signals to thevarious sections of the data processing unit 102 during the operation ofthe data processing unit 102. In certain embodiments, the processor 204also includes memory (not shown) for storing data such as theidentification information for the data processing unit 102, as well asthe data signals received from the sensor 101. The stored informationmay be retrieved and processed for transmission to the primary receiverunit 104 under the control of the processor 204. Furthermore, the powersupply 207 may include a commercially available battery.

The data processing unit 102 is also configured such that the powersupply section 207 is capable of providing power to the data processingunit 102 for a minimum period of time, e.g., at least about one month,e.g., at least about three months or more, of continuous operation. Theminimum may be after (i.e., in addition to) a period of time, e.g., upto about eighteen months, of being stored in a low- or no-power(non-operating) mode. In certain embodiments, this may be achieved bythe processor 204 operating in low power modes in the non-operatingstate, for example, drawing no more than minimal current, e.g.,approximately 1 μA of current or less. In certain embodiments, amanufacturing process of the data processing unit 102 may place the dataprocessing unit 102 in the lower power, non-operating state (i.e.,post-manufacture sleep mode). In this manner, the shelf life of the dataprocessing unit 102 may be significantly improved. Moreover, as shown inFIG. 2, while the power supply unit 207 is shown as coupled to theprocessor 204, and as such, the processor 204 is configured to providecontrol of the power supply unit 207, it should be noted that within thescope of the present disclosure, the power supply unit 207 is configuredto provide the necessary power to each of the components of the dataprocessing unit 102 shown in FIG. 2.

Referring back to FIG. 2, the power supply section 207 of the dataprocessing unit 102 in one embodiment may include a rechargeable batteryunit that may be recharged by a separate power supply recharging unit(for example, provided in the receiver unit 104) so that the dataprocessing unit 102 may be powered for a longer period of usage time. Incertain embodiments, the data processing unit 102 may be configuredwithout a battery in the power supply section 207, in which case thedata processing unit 102 may be configured to receive power from anexternal power supply source (for example, a battery, electrical outlet,etc.) as discussed in further detail below.

Referring yet again to FIG. 2, a temperature detection section 203 ofthe data processing unit 102 is configured to monitor the temperature ofthe skin near the sensor insertion site. The temperature reading may beused to adjust the analyte readings obtained from the analog interface201.

The RF transmitter 206 of the data processing unit 102 may be configuredfor operation in a certain frequency band, e.g., the frequency band of315 MHz to 322 MHz, for example, in the United States. The frequencyband may be the same or different outside the United States. Further, incertain embodiments, the RF transmitter 206 is configured to modulatethe carrier frequency by performing, e.g., Frequency Shift Keying andManchester encoding, and/or other protocol(s). In certain embodiments,the data transmission rate is set for efficient and effectivetransmission. For example, in certain embodiments the data transmissionrate may be about 19,200 symbols per second, with a minimum transmissionrange for communication with the primary receiver unit 104.

Also shown is a leak detection circuit 214 coupled to the guard contact(G) 211 and the processor 204 in the data processing unit 102 of thedata monitoring and management system 100. The leak detection circuit214 may be configured to detect leakage current in the sensor 101 todetermine whether the measured sensor data is corrupt or whether themeasured data from the sensor 101 is accurate. Such detection maytrigger a notification to the user.

FIG. 3 is a block diagram of an embodiment of a receiver/monitor unitsuch as the primary receiver unit 104 of the data monitoring andmanagement system shown in FIG. 1. The primary receiver unit 104includes one or more of a blood glucose test strip interface 301, an RFreceiver 302, an input 303, a temperature detection section 304, and aclock 305, each of which is operatively coupled to a processing andstorage section 307. The primary receiver unit 104 also includes a powersupply 306 operatively coupled to a power conversion and monitoringsection 308. Further, the power conversion and monitoring section 308 isalso coupled to the receiver processor 307. Moreover, also shown are areceiver serial communication section 309, and an output 310, eachoperatively coupled to the processing and storage unit 307. The receivermay include user input and/or interface components or may be free ofuser input and/or interface components.

In certain embodiments, the test strip interface 301 includes a glucoselevel testing portion to receive a blood (or other body fluid sample)glucose test or information related thereto. For example, the interfacemay include a test strip port to receive a glucose test strip. Thedevice may determine the glucose level of the test strip, and optionallydisplay (or otherwise notice) the glucose level on the output 310 of theprimary receiver unit 104. Any suitable test strip may be employed,e.g., test strips that only require a very small amount (e.g., onemicroliter or less, e.g., 0.5 microliter or less, e.g., 0.1 microliteror less), of applied sample to the strip in order to obtain accurateglucose information, e.g. FreeStyle® blood glucose test strips fromAbbott Diabetes Care Inc. Glucose information obtained by the in vitroglucose testing device may be used for a variety of purposes,computations, and the like. For example, the information may be used tocalibrate sensor 101, confirm results of the sensor 101 to increase theconfidence thereof (e.g., in instances in which information obtained bysensor 101 is employed in therapy related decisions).

In further embodiments, the data processing unit 102 and/or the primaryreceiver unit 104 and/or the secondary receiver unit 106, and/or thedata processing terminal/infusion section 105 may be configured toreceive the blood glucose value wirelessly over a communication linkfrom, for example, a blood glucose meter. In further embodiments, a usermanipulating or using the analyte monitoring system 100 (FIG. 1) maymanually input the blood glucose value using, for example, a userinterface (for example, a keyboard, keypad, voice commands, and thelike) incorporated in the one or more of the data processing unit 102,the primary receiver unit 104, secondary receiver unit 106, or the dataprocessing terminal/infusion section 105.

Additional detailed descriptions are provided in U.S. Pat. Nos.5,262,035; 5,262,305; 5,264,104; 5,320,715; 5,593,852; 6,103,033;6,134,461; 6,175,752; 6,560,471; 6,579,690; 6,605,200; 6,654,625;6,746,582; 6,932,894; and in U.S. Published Patent Application No.2004/0186365, now U.S. Pat. No. 7,811,231, the disclosures of each ofwhich are herein incorporated by reference.

FIG. 4 schematically shows an embodiment of an analyte sensor inaccordance with the present disclosure. This sensor embodiment includeselectrodes 401, 402 and 403 on a base 404. Electrodes (and/or otherfeatures) may be applied or otherwise processed using any suitabletechnology, e.g., chemical vapor deposition (CVD), physical vapordeposition, sputtering, reactive sputtering, printing, coating, ablating(e.g., laser ablation), painting, dip coating, etching, and the like.Materials include but are not limited to aluminum, carbon (such asgraphite), cobalt, copper, gallium, gold, indium, iridium, iron, lead,magnesium, mercury (as an amalgam), nickel, niobium, osmium, palladium,platinum, rhenium, rhodium, selenium, silicon (e.g., dopedpolycrystalline silicon), silver, tantalum, tin, titanium, tungsten,uranium, vanadium, zinc, zirconium, mixtures thereof, and alloys,oxides, or metallic compounds of these elements.

The sensor may be wholly implantable in a user or may be configured sothat only a portion is positioned within (internal) a user and anotherportion outside (external) a user. For example, the sensor 400 mayinclude a portion positionable above a surface of the skin 410, and aportion positioned below the skin. In such embodiments, the externalportion may include contacts (connected to respective electrodes of thesecond portion by traces) to connect to another device also external tothe user such as a transmitter unit. While the embodiment of FIG. 4shows three electrodes side-by-side on the same surface of base 404,other configurations are contemplated, e.g., fewer or greaterelectrodes, some or all electrodes on different surfaces of the base orpresent on another base, some or all electrodes stacked together,electrodes of differing materials and dimensions, etc.

FIG. 5A shows a perspective view of an embodiment of an electrochemicalanalyte sensor 500 having a first portion (which in this embodiment maybe characterized as a major portion) positionable above a surface of theskin 510, and a second portion (which in this embodiment may becharacterized as a minor portion) that includes an insertion tip 530positionable below the skin, e.g., penetrating through the skin andinto, e.g., the subcutaneous space 520, in contact with the user'sbiofluid such as interstitial fluid. Contact portions of a workingelectrode 501, a reference electrode 502, and a counter electrode 503are positioned on the portion of the sensor 500 situated above the skinsurface 510. Working electrode 501, a reference electrode 502, and acounter electrode 503 are shown at the second section and particularlyat the insertion tip 530. Traces may be provided from the electrode atthe tip to the contact, as shown in FIG. 5A. It is to be understood thatgreater or fewer electrodes may be provided on a sensor. For example, asensor may include more than one working electrode and/or the counterand reference electrodes may be a single counter/reference electrode,etc.

FIG. 5B shows a cross sectional view of a portion of the sensor 500 ofFIG. 5A. The electrodes 501, 502 and 503, of the sensor 500 as well asthe substrate and the dielectric layers are provided in a layeredconfiguration or construction. For example, as shown in FIG. 5B, in oneaspect, the sensor 500 (such as the sensor unit 101 FIG. 1), includes asubstrate layer 504, and a first conducting layer 501 such as carbon,gold, etc., disposed on at least a portion of the substrate layer 504,and which may provide the working electrode. Also shown disposed on atleast a portion of the first conducting layer 501 is a sensing layer508.

A first insulation layer such as a first dielectric layer 505 isdisposed or layered on at least a portion of the first conducting layer501, and further, a second conducting layer 509 may be disposed orstacked on top of at least a portion of the first insulation layer (ordielectric layer) 505. As shown in FIG. 5B, the second conducting layer509 may provide the reference electrode 502, and in one aspect, mayinclude a layer of silver/silver chloride (Ag/AgCl), gold, etc.

A second insulation layer 506 such as a dielectric layer in oneembodiment may be disposed or layered on at least a portion of thesecond conducting layer 509. Further, a third conducting layer 503 mayprovide the counter electrode 503. It may be disposed on at least aportion of the second insulation layer 506. Finally, a third insulationlayer 507 may be disposed or layered on at least a portion of the thirdconducting layer 503. In this manner, the sensor 500 may be layered suchthat at least a portion of each of the conducting layers is separated bya respective insulation layer (for example, a dielectric layer). Theembodiment of FIGS. 5A and 5B shows the layers having different lengths.Some or all of the layers may have the same or different lengths and/orwidths.

In certain embodiments, some or all of the electrodes 501, 502, 503 maybe provided on the same side of the substrate 504 in the layeredconstruction as described above, or alternatively, may be provided in aco-planar manner such that two or more electrodes may be positioned onthe same plane (e.g., side-by side (e.g., parallel) or angled relativeto each other) on the substrate 504. For example, co-planar electrodesmay include a suitable spacing there between and/or include dielectricmaterial or insulation material disposed between the conductinglayers/electrodes. Furthermore, in certain embodiments one or more ofthe electrodes 501, 502, 503 may be disposed on opposing sides of thesubstrate 504. In such embodiments, contact pads may be on the same ordifferent sides of the substrate. For example, an electrode may be on afirst side and its respective contact may be on a second side, e.g., atrace connecting the electrode and the contact may traverse through thesubstrate.

As noted above, analyte sensors may include an analyte-responsive enzymeto provide a sensing component or sensing layer. Some analytes, such asoxygen, can be directly electrooxidized or electroreduced on a sensor,and more specifically at least on a working electrode of a sensor. Otheranalytes, such as glucose and lactate, require the presence of at leastone electron transfer agent and/or at least one catalyst to facilitatethe electrooxidation or electroreduction of the analyte. Catalysts mayalso be used for those analytes, such as oxygen, that can be directlyelectrooxidized or electroreduced on the working electrode. For theseanalytes, each working electrode includes a sensing layer (see forexample sensing layer 508 of FIG. 5B) proximate to or on a surface of aworking electrode. In many embodiments, a sensing layer is formed nearor on only a small portion of at least a working electrode.

The sensing layer includes one or more components designed to facilitatethe electrochemical oxidation or reduction of the analyte. The sensinglayer may include, for example, a catalyst to catalyze a reaction of theanalyte and produce a response at the working electrode, an electrontransfer agent to transfer electrons between the analyte and the workingelectrode (or other component), or both.

A variety of different sensing layer configurations may be used. Incertain embodiments, the sensing layer is deposited on the conductivematerial of a working electrode. The sensing layer may extend beyond theconductive material of the working electrode. In some cases, the sensinglayer may also extend over other electrodes, e.g., over the counterelectrode and/or reference electrode (or counter/reference is provided).

A sensing layer that is in direct contact with the working electrode maycontain an electron transfer agent to transfer electrons directly orindirectly between the analyte and the working electrode, and/or acatalyst to facilitate a reaction of the analyte. For example, aglucose, lactate, or oxygen electrode may be formed having a sensinglayer which contains a catalyst, such as glucose oxidase, lactateoxidase, or laccase, respectively, and an electron transfer agent thatfacilitates the electrooxidation of the glucose, lactate, or oxygen,respectively.

Examples of sensing layers that may be employed are described in U.S.patents and applications noted herein, including, e.g., in U.S. Pat.Nos. 5,262,035; 5,264,104; 5,543,326; 6,605,200; 6,605,201; 6,676,819;and 7,299,082; the disclosures of which are herein incorporated byreference.

In other embodiments the sensing layer is not deposited directly on theworking electrode. Instead, the sensing layer may be spaced apart fromthe working electrode, and separated from the working electrode, e.g.,by a separation layer. A separation layer may include one or moremembranes or films or a physical distance. In addition to separating theworking electrode from the sensing layer the separation layer may alsoact as a mass transport limiting layer and/or an interferent eliminatinglayer and/or a biocompatible layer.

Exemplary mass transport layers are described in U.S. patents andapplications noted herein, including, e.g., in U.S. Pat. Nos. 5,593,852;6,881,551; and 6,932,894, the disclosures of which are hereinincorporated by reference.

In certain embodiments which include more than one working electrode,one or more of the working electrodes may not have a correspondingsensing layer, or may have a sensing layer which does not contain one ormore components (e.g., an electron transfer agent and/or catalyst)needed to electrolyze the analyte. Thus, the signal at this workingelectrode may correspond to background signal which may be removed fromthe analyte signal obtained from one or more other working electrodesthat are associated with fully-functional sensing layers by, forexample, subtracting the signal.

In certain embodiments, the sensing layer includes one or more electrontransfer agents. Electron transfer agents that may be employed areelectroreducible and electrooxidizable ions or molecules having redoxpotentials that are a few hundred millivolts above or below the redoxpotential of the standard calomel electrode (SCE). The electron transferagent may be organic, organometallic, or inorganic. Examples of organicredox species are quinones and species that in their oxidized state havequinoid structures, such as Nile blue and indophenol. Examples oforganometallic redox species are metallocenes such as ferrocene.Examples of inorganic redox species are hexacyanoferrate (III),ruthenium hexamine, etc.

In certain embodiments, electron transfer agents have structures orcharges which prevent or substantially reduce the diffusional loss ofthe electron transfer agent during the period of time that the sample isbeing analyzed. For example, electron transfer agents include, but arenot limited to, a redox species, e.g., bound to a polymer which can inturn be disposed on or near the working electrode. The bond between theredox species and the polymer may be covalent, coordinative, or ionic.Although any organic, organometallic or inorganic redox species may bebound to a polymer and used as an electron transfer agent, in certainembodiments the redox species is a transition metal compound or complex,e.g., osmium, ruthenium, iron, and cobalt compounds or complexes. Itwill be recognized that many redox species described for use with apolymeric component may also be used, without a polymeric component.

One type of polymeric electron transfer agent contains a redox speciescovalently bound in a polymeric composition. An example of this type ofmediator is poly(vinylferrocene). Another type of electron transferagent contains an ionically-bound redox species. This type of mediatormay include a charged polymer coupled to an oppositely charged redoxspecies. Examples of this type of mediator include a negatively chargedpolymer coupled to a positively charged redox species such as an osmiumor ruthenium polypyridyl cation. Another example of an ionically-boundmediator is a positively charged polymer such as quaternizedpoly(4-vinyl pyridine) or poly(1-vinyl imidazole) coupled to anegatively charged redox species such as ferricyanide or ferrocyanide.In other embodiments, electron transfer agents include a redox speciescoordinatively bound to a polymer. For example, the mediator may beformed by coordination of an osmium or cobalt 2,2′-bipyridyl complex topoly(1-vinyl imidazole) or poly(4-vinyl pyridine).

Suitable electron transfer agents are osmium transition metal complexeswith one or more ligands, each ligand having a nitrogen-containingheterocycle such as 2,2′-bipyridine, 1,10-phenanthroline, 1-methyl,2-pyridyl biimidazole, or derivatives thereof. The electron transferagents may also have one or more ligands covalently bound in a polymer,each ligand having at least one nitrogen-containing heterocycle, such aspyridine, imidazole, or derivatives thereof. One example of an electrontransfer agent includes (a) a polymer or copolymer having pyridine orimidazole functional groups and (b) osmium cations complexed with twoligands, each ligand containing 2,2′-bipyridine, 1,10-phenanthroline, orderivatives thereof, the two ligands not necessarily being the same.Some derivatives of 2,2′-bipyridine for complexation with the osmiumcation include, but are not limited to, 4,4′-dimethyl-2,2′-bipyridineand mono-, di-, and polyalkoxy-2,2′-bipyridines, such as4,4′-dimethoxy-2,2′-bipyridine. Derivatives of 1,10-phenanthroline forcomplexation with the osmium cation include, but are not limited to,4,7-dimethyl-1,10-phenanthroline and mono, di-, andpolyalkoxy-1,10-phenanthrolines, such as4,7-dimethoxy-1,10-phenanthroline. Polymers for complexation with theosmium cation include, but are not limited to, polymers and copolymersof poly(1-vinyl imidazole) (referred to as “PVI”) and poly(4-vinylpyridine) (referred to as “PVP”). Suitable copolymer substituents ofpoly(1-vinyl imidazole) include acrylonitrile, acrylamide, andsubstituted or quaternized N-vinyl imidazole, e.g., electron transferagents with osmium complexed to a polymer or copolymer of poly(1-vinylimidazole).

Embodiments may employ electron transfer agents having a redox potentialranging from about −200 mV to about +200 mV versus the standard calomelelectrode (SCE). The sensing layer may also include a catalyst which iscapable of catalyzing a reaction of the analyte. The catalyst may also,in some embodiments, act as an electron transfer agent. One example of asuitable catalyst is an enzyme which catalyzes a reaction of theanalyte. For example, a catalyst, such as a glucose oxidase, glucosedehydrogenase (e.g., pyrroloquinoline quinone (PQQ), dependent glucosedehydrogenase, flavine adenine dinucleotide (FAD), or nicotinamideadenine dinucleotide (NAD) dependent glucose dehydrogenase), may be usedwhen the analyte of interest is glucose. A lactate oxidase or lactatedehydrogenase may be used when the analyte of interest is lactate.Laccase may be used when the analyte of interest is oxygen or whenoxygen is generated or consumed in response to a reaction of theanalyte.

The sensing layer may also include a catalyst which is capable ofcatalyzing a reaction of the analyte. The catalyst may also, in someembodiments, act as an electron transfer agent. One example of asuitable catalyst is an enzyme which catalyzes a reaction of theanalyte. For example, a catalyst, such as a glucose oxidase, glucosedehydrogenase (e.g., pyrroloquinoline quinone (PQQ), dependent glucosedehydrogenase or oligosaccharide dehydrogenase, flavine adeninedinucleotide (FAD) dependent glucose dehydrogenase, nicotinamide adeninedinucleotide (NAD) dependent glucose dehydrogenase), may be used whenthe analyte of interest is glucose. A lactate oxidase or lactatedehydrogenase may be used when the analyte of interest is lactate.Laccase may be used when the analyte of interest is oxygen or whenoxygen is generated or consumed in response to a reaction of theanalyte.

In certain embodiments, a catalyst may be attached to a polymer, crosslinking the catalyst with another electron transfer agent (which, asdescribed above, may be polymeric). A second catalyst may also be usedin certain embodiments. This second catalyst may be used to catalyze areaction of a product compound resulting from the catalyzed reaction ofthe analyte. The second catalyst may operate with an electron transferagent to electrolyze the product compound to generate a signal at theworking electrode. Alternatively, a second catalyst may be provided inan interferent-eliminating layer to catalyze reactions that removeinterferents.

Certain embodiments include a Wired Enzyme™ sensing layer (AbbottDiabetes Care Inc.) that works at a gentle oxidizing potential, e.g., apotential of about +40 mV. This sensing layer uses an osmium (Os)-basedmediator designed for low potential operation and is stably anchored ina polymeric layer. Accordingly, in certain embodiments, the sensingelement is a redox active component that includes (1) Osmium-basedmediator molecules attached by stable (bidente) ligands anchored to apolymeric backbone, and (2) glucose oxidase enzyme molecules. These twoconstituents are crosslinked together.

A mass transport limiting layer (not shown), e.g., an analyte fluxmodulating layer, may be included with the sensor to act as adiffusion-limiting barrier to reduce the rate of mass transport of theanalyte, for example, glucose or lactate, into the region around theworking electrodes. The mass transport limiting layers are useful inlimiting the flux of an analyte to a working electrode in anelectrochemical sensor so that the sensor is linearly responsive over alarge range of analyte concentrations and is easily calibrated. Masstransport limiting layers may include polymers and may be biocompatible.A mass transport limiting layer may provide many functions, e.g.,biocompatibility and/or interferent-eliminating, etc.

In certain embodiments, a mass transport limiting layer is a membranecomposed of crosslinked polymers containing heterocyclic nitrogengroups, such as polymers of polyvinylpyridine and polyvinylimidazole.Embodiments also include membranes that are made of a polyurethane, orpolyether urethane, or chemically related material, or membranes thatare made of silicone, and the like.

A membrane may be formed by crosslinking in situ a polymer, modifiedwith a zwitterionic moiety, a non-pyridine copolymer component, andoptionally another moiety that is either hydrophilic or hydrophobic,and/or has other desirable properties, in an alcohol-buffer solution.The modified polymer may be made from a precursor polymer containingheterocyclic nitrogen groups. For example, a precursor polymer may bepolyvinylpyridine or polyvinylimidazole. Optionally, hydrophilic orhydrophobic modifiers may be used to “fine-tune” the permeability of theresulting membrane to an analyte of interest. Optional hydrophilicmodifiers, such as poly(ethylene glycol), hydroxyl or polyhydroxylmodifiers, may be used to enhance the biocompatibility of the polymer orthe resulting membrane.

A membrane may be formed in situ by applying an alcohol-buffer solutionof a crosslinker and a modified polymer over an enzyme-containingsensing layer and allowing the solution to cure for about one to twodays or other appropriate time period. The crosslinker-polymer solutionmay be applied to the sensing layer by placing a droplet or droplets ofthe solution on the sensor, by dipping the sensor into the solution, orthe like. Generally, the thickness of the membrane is controlled by theconcentration of the solution, by the number of droplets of the solutionapplied, by the number of times the sensor is dipped in the solution, orby any combination of these factors. A membrane applied in this mannermay have any combination of the following functions: (1) mass transportlimitation, i.e., reduction of the flux of analyte that can reach thesensing layer, (2) biocompatibility enhancement, or (3) interferentreduction.

The electrochemical sensors may employ any suitable measurementtechnique. For example, may detect current or may employ potentiometry.Techniques may include, but are not limited to, amperometry, coulometry,and voltammetry. In some embodiments, sensing systems may be optical,colorimetric, and the like.

In certain embodiments, the sensing system detects hydrogen peroxide toinfer glucose levels. For example, a hydrogen peroxide-detecting sensormay be constructed in which a sensing layer includes enzyme such asglucose oxides, glucose dehydrogenase, or the like, and is positionedproximate to the working electrode. The sensing layer may be covered bya membrane that is selectively permeable to glucose. Once the glucosepasses through the membrane, it is oxidized by the enzyme and reducedglucose oxidase can then be oxidized by reacting with molecular oxygento produce hydrogen peroxide.

Certain embodiments include a hydrogen peroxide-detecting sensorconstructed from a sensing layer prepared by crosslinking two componentstogether, for example: (1) a redox compound such as a redox polymercontaining pendent Os polypyridyl complexes with oxidation potentials ofabout +200 mV vs. SCE, and (2) periodate oxidized horseradish peroxidase(HRP). Such a sensor functions in a reductive mode; the workingelectrode is controlled at a potential negative to that of the Oscomplex, resulting in mediated reduction of hydrogen peroxide throughthe HRP catalyst.

In another example, a potentiometric sensor can be constructed asfollows. A glucose-sensing layer is constructed by crosslinking together(1) a redox polymer containing pendent Os polypyridyl complexes withoxidation potentials from about −200 mV to +200 mV vs. SCE, and (2)glucose oxidase. This sensor can then be used in a potentiometric mode,by exposing the sensor to a glucose containing solution, underconditions of zero current flow, and allowing the ratio ofreduced/oxidized Os to reach an equilibrium value. The reduced/oxidizedOs ratio varies in a reproducible way with the glucose concentration,and will cause the electrode's potential to vary in a similar way.

A sensor may also include an active agent such as an anticlotting and/orantiglycolytic agent(s) disposed on at least a portion of a sensor thatis positioned in a user. An anticlotting agent may reduce or eliminatethe clotting of blood or other body fluid around the sensor,particularly after insertion of the sensor. Examples of usefulanticlotting agents include heparin and tissue plasminogen activator(TPA), as well as other known anticlotting agents. Embodiments mayinclude an antiglycolytic agent or precursor thereof. Examples ofantiglycolytic agents are glyceraldehyde, fluoride ion, and mannose.

Sensors may be configured to require no system calibration or no usercalibration. For example, a sensor may be factory calibrated and neednot require further calibrating. In certain embodiments, calibration maybe required, but may be done without user intervention, i.e., may beautomatic. In those embodiments in which calibration by the user isrequired, the calibration may be according to a predetermined scheduleor may be dynamic, i.e., the time for which may be determined by thesystem on a real-time basis according to various factors, such as, butnot limited to, glucose concentration and/or temperature and/or rate ofchange of glucose, etc.

Calibration may be accomplished using an in vitro test strip (or otherreference), e.g., a small sample test strip such as a test strip thatrequires less than about 1 microliter of sample (for example FreeStyle®blood glucose monitoring test strips from Abbott Diabetes Care Inc.).For example, test strips that require less than about 1 nanoliter ofsample may be used. In certain embodiments, a sensor may be calibratedusing only one sample of body fluid per calibration event. For example,a user need only lance a body part one time to obtain sample for acalibration event (e.g., for a test strip), or may lance more than onetime within a short period of time if an insufficient volume of sampleis firstly obtained. Embodiments include obtaining and using multiplesamples of body fluid for a given calibration event, where glucosevalues of each sample are substantially similar. Data obtained from agiven calibration event may be used independently to calibrate orcombined with data obtained from previous calibration events, e.g.,averaged including weighted averaged, etc., to calibrate. In certainembodiments, a system need only be calibrated once by a user, whererecalibration of the system is not required.

Calibration and validation protocols for the calibration and validationof in vivo continuous analyte systems including analyte sensors, forexample, are described in e.g., U.S. Pat. Nos. 6,284,478; 7,299,082; andU.S. patent application Seri. No. 11/365,340, now U.S. Pat. No.7,885,698; Ser. No. 11/537,991, now U.S. Pat. No. 7,618,369; Ser. Nos.11/618,706; 12/242,823, now U.S. Pat. No. 8,219,173; and Ser. No.12/363,712, now U.S. Pat. No. 8,346,335, the disclosures of each ofwhich are herein incorporated by reference.

Analyte systems may include an optional alarm system that, e.g., basedon information from a processor, warns the patient of a potentiallydetrimental condition of the analyte. For example, if glucose is theanalyte, an alarm system may warn a user of conditions such ashypoglycemia and/or hyperglycemia and/or impending hypoglycemia, and/orimpending hyperglycemia. An alarm system may be triggered when analytelevels approach, reach or exceed a threshold value. An alarm system mayalso, or alternatively, be activated when the rate of change, oracceleration of the rate of change, in analyte levels increases ordecreases, approaches, reaches or exceeds a threshold rate oracceleration. A system may also include system alarms that notify a userof system information such as battery condition, calibration, sensordislodgment, sensor malfunction, etc. Alarms may be, for example,auditory and/or visual. Other sensory-stimulating alarm systems may beused including alarm systems which heat, cool, vibrate, or produce amild electrical shock when activated.

The embodiments of the present disclosure also include sensors used insensor-based drug delivery systems. The system may provide a drug tocounteract the high or low level of the analyte in response to thesignals from one or more sensors. Alternatively, the system may monitorthe drug concentration to ensure that the drug remains within a desiredtherapeutic range. The drug delivery system may include one or more(e.g., two or more) sensors, a processing unit such as a transmitter, areceiver/display unit, and a drug administration system. In some cases,some or all components may be integrated in a single unit. Asensor-based drug delivery system may use data from the one or moresensors to provide necessary input for a control algorithm/mechanism toadjust the administration of drugs, e.g., automatically orsemi-automatically. As an example, a glucose sensor may be used tocontrol and adjust the administration of insulin from an external orimplanted insulin pump.

As is well established, HbA1C (also referred to as A1C) is the standardmetric for determining an individual's glycemic control. Studies haverecently derived relationships of HbA1C to mean blood glucose levels.The advent of continuous glucose monitoring (CGM) has enabled accurateand continuous measurements of mean glucose levels over extended periodsof time.

It has been shown that controlling HbA1C levels as close to a normallevel as possible is important to reduce the risk of diabeticcomplications. However, it is generally difficult to achieve the tightglycemic control necessary to obtain the desired reduction in HbA1Clevels without potentially increasing the risk of hypoglycemiccondition. In one aspect, mean glucose values may be associated orcorrelated with the HbA1C levels. For example, a slope of 36 mg/dL per1% HbA1C illustrates the relationship between the regression analysisrelating HbA1C level to mean glucose values. Further, a lower slope ofapproximately 18 mg/dL may indicate the relationship between HbA1C leveland mean glucose values. Additionally, variability may exist betweendiabetic patients as pertains to the relationship between the HbA1Clevel and mean glucose values, indicating a potentially individualizedcharacteristic of the rate of protein glycation that may effect longterm complications of poorly controlled diabetic condition. Othervariables such as race and ethnicity also may have effect in the HbA1Clevel adjusted for glycemic indices.

Accordingly, embodiments of the present disclosure include improvementin the HbA1C level estimation with the knowledge or information of thepatient's individualized relationship between HbA1C level and the meanglucose values.

In one aspect, a diabetic patient or a subject with a lower slope(showing the relationship between HbA1C level and means glucose values)may be able to achieve a greater improvement in HbA1C level for a givendecrease in average glucose levels, as compared with a patient with ahigher slope. As such, the patient with the lower slope may be able toachieve a reduced risk of chronic diabetic complications by lower HbA1Clevel with a minimal increase in the risk of potentially severehypoglycemia (due to a relatively modest reduction in the averageglucose values in view of their lower slope).

Given the individualized information related to a patient's averageglucose value relative to the HbA1C level, a physician or a careprovider in one aspect may determine a suitable glycemic target for theparticular patient such that the calculated reduction in the HbA1C levelmay be attained while minimizing the risk of severe hypoglycemia.

In one aspect, in the analyte monitoring system 100 (FIG. 1), a bloodglucose meter or monitor with sufficient data capacity for storing andprocessing glucose values, or a data processing terminal 105 (FIG. 1)with data management capability such as, for example, CoPilot™ HealthManagement Software available from Abbott Diabetes Care Inc., ofAlameda, Calif., may be configured to provide improved glycemic controlbased on mean glucose values and HbA1C levels. For example, in oneaspect, an HbA1C measurement may be obtained either manually entered ordownloaded from the patient's medical records, and an average glucoselevel is calculated over a predetermined time period (such as 30 days,45 days, 60 days, 90 days and so on).

With the average glucose level information, a patient's individualrelationship between average glucose and HbA1C (or other glycatedproteins) may be determined. The determined individual relationship maybe represented or output as a slope (lower slope or higher slope ingraphical representation, for example), based upon a line fit to two ormore determinations of average glucose and HbA1C, for example.

Alternatively, the individualized relationship may be based upon asingle assessment of average glucose level and HbA1C and an interceptvalue, which may correspond to an HbA1C of zero at zero mean glucoselevel. Based on this, the physician or the health care provider (or theanalyte monitoring device of data management software) may determineappropriate or suitable individualized glycemic targets to achieve thedesired reductions in HbA1C without the undesired risk of severehypoglycemia. In one aspect, the analysis may be repeated one or moretimes (for example, quarterly with each regularly scheduled HbA1C test)to update the glycemic targets so as to optimize therapy management andtreatment, and to account for or factor in any intra-person variability.

In this manner, in one aspect, there is provided a systematic andindividualized approach to establish and update glycemic targets basedupon the relationship between the mean glucose values (as may bedetermined using a continuous glucose monitoring system or a discrete invitro blood glucose meter test) and their HbA1C level, and adetermination of an acceptable level of risk of severe hypoglycemia.

Accordingly, embodiments of the present disclosure provideindividualized glycemic targets to be determined for a particularpatient based upon their individualized rate of protein glycation,measured by the relationship between the mean glucose values and theHbA1C levels, such that the physician or the care provider, or theanalyte monitoring system including data management software, forexample, may determine the glycemic targets to achieve the desiredreduction in HbA1C level without the unnecessary risk for hypoglycemiccondition.

Additionally, based on the information or individualized relationshipdiscussed above, embodiments of the present disclosure may be used toimprove the estimation of subsequent HbA1C values based upon measured ormonitored glucose values of a patient. In this manner the HbA1C levelestimation may be improved by using the patient's individualizedrelationship between prior or past HbA1C levels, and mean glucose valuesto more accurately predict or estimate current HbA1C levels.

In this manner, in aspects of present disclosure, the HbA1C levelestimation may be improved or enhanced based on a predeterminedindividualized relationship between a patient's average glucose valuesand their HbA1C and the current mean glucose level.

Experimental Study #1

Eighty eight (88) subjects (out of a total 90 enrolled subjects N) usedthe FreeStyle Navigator® Continuous Glucose Monitoring (CGM) system overa 90 day period to obtain CGM system data and to perform discrete bloodglucose measurements using the Freestyle® blood glucose meter built intothe receiver of the CGM system for sensor calibration, confirmation ofglucose related notifications or alarms, and insulin therapyadjustments. Threshold and projected alarms were enabled and subjectswere not blinded to the real time monitored glucose data.

Mean CGM glucose data and self-monitoring of discrete blood glucose(SMBG) test readings were obtained over a 90 day period. Therelationship between the mean glucose level and HbA1C level wasdetermined for 88 subjects with Type 1 diabetes over this time period.Overall, 4.3±3.9 (mean±standard deviation (SD)) SMBG and 95.0±61.5 CGMreadings were collected each day. Including only patient-days with atleast one CGM (6194/7920) or SMBG (6197/7920) value, 5.4±3.5 SMBG and121.5±40.2 CGM readings per day were obtained and available.

Equations for least-square linear regression fits of CGM and SMBGmeasurements to HbA1C were similar:

(mean glucose)=(slope±1SE)*HbA1C+(intercept±1SE)

mean CGM [mg/dL]=20.5±2.1*A1C+5.2±14.7, r ²=0.52

mean SMBG [mg/dL]=19.0±2.6*A1C+16.2±18.1, r ²=0.38

These slopes of 19.0 and 20.5 (mg/dL)/% differ markedly from theAmerican Diabetes Association (ADA) value of 35.6 (mg/dL)/%, but aresimilar to reports from recent studies using CGM data. Mean CGM and meanSMBG levels were found to be closely correlated:

mean CGM [mg/dL]=(0.80±0.04)*mean SMBG+(27.9±6.4), r ²=0.80

The low slope of less than 1 for mean CGM data compared to mean SMBGlevels may indicate the measurement selection bias of SMBG levels beforeand after meals and in response to CGM system alarms or notification.This bias did not greatly affect the relationship to HbA1C levels.However, mean CGM data correlated more closely to the HbA1C levels andthus is a better indicator of the HbA1C level.

That the CGM data had an r² (Pearson's correlation coefficient) value ofonly 0.52 indicates that individual differences in rates of proteinglycation at a given blood glucose concentration may be an importantfactor when addressing glycemic control. The individual differences maybe relevant in determining risk of future diabetic complications, andmay suggest personalized goals of mean glucose for a given HbA1C target.

Referring now to the Figures, FIG. 6 provides a tabular illustration ofthe demographic and characteristics of participants in the 90 dayscontinuous glucose monitoring system use study in one aspect. As can beseen from the table shown in FIG. 6, the 88 subjects for the 90 daystudy were selected to cover a wide range of characteristics typical forthe general population of people with diabetic conditions, and whogenerally have a controlled diabetic condition, with a maximum HbA1Clevel of 9.1%.

FIG. 7 is a chart illustrating the relationship between the mean 90 daycontinuously monitored glucose level and the mean 90 day discrete bloodglucose test results compared with the HbA1C level in one aspect.Referring to FIG. 7, it can be seen that the CGM data and the SMBGreadings were observed to have similar relationship to HbA1C levels,despite the less frequency of the SMBG readings. However, the level ofthe relationship to the HbA1C levels are relatively moderate, indicatingother variables which may affect the relationship, including, forexample, genetic factors that may impact the glycation of the hemoglobinmolecule in the presence of glucose, or individuals may have longer orshorter average erythrocyte lifespans.

Referring to FIG. 7, those individuals whose glucose values are abovethe line of the mean relationship as shown can tolerate more glucosewithout increasing their HbA1C level, while those individuals whoseglucose values are below the mean relationship line experience increasesin HbA1C level at lower than expected blood glucose concentrations. Inone aspect, the rate of glycation based on a 90 day (or some othersuitable time range) mean glucose level divided by the HbA1C level mayprovide useful guidance in therapy decisions.

FIG. 8 provides a graphical illustration of the individual rates ofglycation distribution in one aspect. Referring to FIG. 8, the rate ofglycation including the 90 day mean glucose value divided by the HbA1Clevel characterizes an individual's sensitivity to changes in HbA1Clevel at a given blood glucose concentration. FIG. 8 illustrates thedistribution of rates of glycation for the subjects in the study. Asshown, approximately 15% of the participants may be considered“sensitive glycators” with a glycation ratio of approximately 19 orless. These individuals would need to maintain their blood glucose levelto a lower-than-average value to maintain relatively the same HbA1Clevel as other individuals. For example, if the glycation ratio is 15,than the mean blood glucose level must be kept at approximately 75 mg/dLto expect an HbA1C level of approximately 5%.

Referring again to FIG. 8, approximately 22% of the study participantsmay be considered “insensitive glycators” with a glycation ratio ofapproximately 23 or more. That is, these insensitive glycators may keeptheir blood glucose higher-than-average level and maintain approximatelythe same HbA1C level as other individuals. For example, if the glycationratio is 25, then the mean blood glucose level can be maintained atapproximately 125 mg/dL to expect an HbA1C level of approximately 5%.

FIG. 9 provides a graphical illustration of the slope and correlation ofthe continuously monitored glucose level to the HbA1C level on a weeklybasis in one aspect. HbA1C is considered to be weighted average of bloodglucose levels for the 90 day period based on the average lifespan oferythrocytes. The weighted average, however, may or may not be a linearrelationship. More recent blood glucose levels may influence the HbA1Clevel more strongly (thus weighting more heavily) than the more distant(in time) blood glucose levels. FIG. 9 illustrates the Pearson'scorrelation (r²) and linear regression slope for each of the 12 weeksprior to the HbA1C measurement. The horizontal lines as shown in theFigure illustrate the values when all weeks in the study are pooledtogether. From FIG. 9, it can be seen that the more recent weeks (forexample, weeks 6 to 13) have a stronger influence on HbA1C level (thatis, having a higher correlation and slope) than the more distant weeks(for example weeks 1 to 5).

FIG. 10 is a graphical illustration of the frequency of the obtainedglucose levels between the SMBG (self monitored blood glucose)measurements and the CGM (continuously monitored glucose) measurement ona daily basis in one aspect. That is, the episodic measurements (SMBG)compared to the continuous measurements (CGM) in the study are shown inthe Figure. The frequency of the glucose levels per day is shown for thetwo measurements. As can be seen, on average, 5.4 SMBG measurements wereperformed per day (e.g., once per 4.4 hours) compared to 121.5 CGMmeasurements per day (once per 12 minutes).

FIG. 11 is a graphical illustration of the glucose measurementdistribution by time of day between the SMBG (self monitored bloodglucose) measurements and the CGM (continuously monitored glucose)measurement in one aspect. As shown in FIG. 11, on average, it can beseen that the SMBG measurements were performed during the day, withspikes near typical meal times, as compared to substantially steadycontinuous CGM measurements.

Experimental Study #1 Results

Based on data collected over the 90 day period, the followingobservations and results were determined. A correlation between HbA1Cand mean glucose was observed, consistent with the indication that HbA1Clevel reflects the integral of blood glucose level over time. Similarslopes for the linear regression fits of CGM data and SMBG measurementsto HbA1C of 20.5 and 19.0 (mg/dL)/%, respectively were observed.Further, both slopes were lower than the 35.6 (mg/dL)/% from HbA1Cvalues paired with 7-point profiles from 1,439 subjects, but consistentwith other studies using CGM data. Moreover, the weaker correlation ofmean glucose level to HbA1C with SMBG values indicates that infrequentand inconsistently timed glucose measurements (SMBG) may not accuratelyreflect glucose concentrations over time as well as CGM data.Additionally, the results indicate an inter-individual variability inglycation rates or erythrocyte survival.

This study of 88 subjects with Type 1 diabetes mellitus and widelyvarying HbA1C levels demonstrated a strong correlation between CGM dataaveraged over the preceding 90 days and HbA1C level. Study subjects werecompliant, using the FreeStyle Navigator® Continuous Glucose MonitoringSystem on greater than 78% of study days and logging an average of 121.5CGM readings per day (CGM readings recorded every 10 minutes) on dayswith at least one CGM value.

Results from the studies have demonstrated that the rate ofmicrovascular complications is correlated with HbA1C levels. Re-analysisof this data also indicates that mean glucose is correlated withmacrovascular complications. Whereas real-time monitored CGM data maysignificantly improve the management of diabetes through theavailability of glucose values, trend indicators, and alarms/alerts, itmay be also used for the determination of mean glucose level and for theprediction of HbA1C level. These metrics have been shown to tracklong-term complications and are essential for physiological condition ortherapy management.

Improved understanding of inter- and intra-individual variation in therelationship between mean glucose level and HbA1C level may be useful inthe determination of glucose targets designed to optimize both thereduction in an individual's risk of the long-term complications ofdiabetes and their short-term risk of hypoglycemia. For example,patients with different relationships between mean glucose and HbA1C maybe able to achieve similar reductions in the risk of microvascularcomplications of diabetes with markedly different decreases in meanglucose, with those patients with the lowest ratios of mean glucose toHbA1C experiencing the least risk of hypoglycemia.

Experimental Study #2

In this experimental study, the objective was to assess glucose control.Threshold and projected alarms were enabled and subjects were notblinded to the glucose data. HbA1C measurements were obtained at thebeginning of the study and at the end of the study.

Data collected from the use of FreeStyle Navigator® Continuous GlucoseMonitoring System was evaluated under home use conditions. In thismulti-center study 90 subjects with Type 1 diabetes wore the continuousglucose monitor (CGM) for 3 months. Fifty-six percent of the subjectswere female and the average age was 42 years (range 18-72). At baseline,38% of the subjects had HbA1C values <7.0%.

Questionnaires were completed at baseline, day 30 and day 90. Subjectswere provided with no additional therapeutic instructions other than tomake treatment decisions based on confirmatory blood glucose tests.HbA1C was measured by a central laboratory at baseline and 90 days.One-minute continuous glucose values were used to assess the glycemicprofiles of study subjects.

Subjects were trained in a clinic visit of approximately 2 hours.Ninety-nine percent reported being confident in CGM use based on thetraining Subjects inserted the sensors in the arm or abdomen with themost common adverse symptom being insertion site bleeding (59 episodesin 22 subjects). After 90 days, 92% reported an overall positive systemexperience. The most important system features to the study subjectswere the glucose readings, glucose alarms and trend arrows.

Both subjects with baseline HbA1C≧8% (p=0.0036) and subjects withbaseline HbA1C<8% (p=0.0001) had significant decreases in their HbA1Cvalue after 90 days. The mean A1C decrease for subjects with baselinevalues of ≧8% was three times greater (−0.6%) than that of the subjectswith baseline values of <8% (−0.2%; p=0.004).

After 90 days, 73% of subjects reported viewing the CGM data displaymore than 12 times per day. There was a direct correlation betweensubject's display reviews per day and corresponding HbA1C change. Theimprovement in glucose control was reflected in HbA1C changes after 90days of CGM use with 55% of subjects reaching a target HbA1C value of<7.0%. The more frequently the patients viewed their glucose results, ingeneral, the greater the improvement in HbA1C values. At baseline thesubjects with an HbA1C of <7.0% had characteristics similar to those ofsubjects with an HbA1C of ≧7.0%. Eight-nine (89) percent of the subjectswere Caucasian. Most subjects (72%) had completed a 4-year collegedegree.

Referring now to the Figures, FIG. 12 is a tabular illustration of thestudy subject characteristics by baseline HbA1C level in one aspect. Itcan be seen from FIG. 12 that the participants of the study had aninitial in-target (defined by the American Diabetes Association (ADA))HbA1C level of <7%, where similar in gender, age, BMI (body mass index),and diabetes duration, compared to the participants in the study who hadan above-target HbA1C level of >7% at the beginning of the study.

FIG. 13 is a graphical illustration of the increase in the number ofstudy subjects that achieved in-target HbA1C during the 90 day studyduration in one aspect. Referring to FIG. 13, it can be observed thatduring the 90 day study duration, the number of participants able toachieve an in-target HbA1C level increased from approximately 40% toapproximately 57%.

FIG. 14 is a graphical illustration of the difference between the meanglucose levels of subjects with in-target HbA1C level (1420) compared toabove-target HbA1C level (1410) during the study duration of 90 days inone aspect. Referring to FIG. 14, during the 90 day study period, theparticipants with the initial in-target HbA1C level (1420) (as discussedabove) had a lower mean glucose level than those with an initialabove-target HbA1C level (1410). The weekly mean glucose level remainedrelatively stable for these participants with the initial in-targetHbA1C level (1420), as compared with the participants with an abovetarget HbA1C level (1410) whose weekly mean glucose level was relativelyhigher and increased towards the end of the 90 day study period.

FIG. 15 is a graphical illustration of the glucose variation betweensubjects with in-target HbA1C level (1520) compared to above-targetHbA1C level (1510) during the study duration of 90 days in one aspect.It can be seen from FIG. 15 that during the study duration, theparticipants who had initial in-target HbA1C level (1520) had a lowerglucose variation (measured by standard deviation per week), than thosewith an above-target HbA1C level (1510), and the glucose values remainedrelatively stable over study period.

FIG. 16 is a graphical illustration of the average percentage HbA1Clevel change based on the number of times the study subjects viewed thecontinuously monitored glucose level in one aspect. It can be seen fromFIG. 16 that the average change in HbA1C level during the 90 day studyperiod for the participants as correlated with the number of times perday the participants reported viewing or seeing the real time CGM datadisplay. It can be observed that the participants that were viewing themonitored glucose levels had their HbA1C levels reduced relatively morethan those who viewed the monitored glucose levels less frequently.

It can be seen that over the course of the 90 day study period using theCGM system, subjects/participants who reported viewing the displayscreen more frequently tended to have more improvement in HbA1C (FIG.16), consistent with the time spent in euglycemia and glucose standarddeviation demonstrated during the study (see, e.g., FIGS. 17-18).

FIG. 17 graphically illustrates the weekly glycemic control resultsbased on the number of times daily the subjects viewed the real timecontinuously monitored glucose levels in one aspect. Referring to FIG.17, the graphical illustration provides the glycemic control (i.e.,measured as the percentage of time between 70 to 180 mg/dL) per week forparticipants associated with the number of times per day theparticipants reported viewing or looking at the continuously monitoredreal time glucose (CGM) data. It can be observed that the participantsthat viewed the glucose data less frequently (1730) had relatively moredegraded glycemic control, with approximately 60% of the time spent ineuglycemia condition during the first week of the study, down toapproximately 50% of time spent in euglycemia in the last week, comparedwith the participants that viewed the glucose data more frequently(1710, 1720).

FIG. 18 is a graphical illustration of the glycemic variability measuredas the standard deviation on a weekly basis of the subjects between thenumber of times daily the subjects viewed the real time continuouslymonitored glucose levels in one aspect. Again, it can be observed thatbased on the glycemic variability per week associated with the number oftimes per day they reported viewing or looking at the CGM data as shownin FIG. 18, the participants that viewed the real time glucose data lessfrequently had degraded glycemic variability (1830), compared with theparticipants that viewed the glucose data more frequently (1810, 1820).

Experimental Study #2 Results

Based on the foregoing, it can be observed that improvement in glucosecontrol resulted in HbA1C changes after 3 months of CGM system use. Forexample, subjects/participants that reported viewing the display screenmore frequently trended toward having greater improvement in HbA1Clevel. Although subjects were not provided therapeutic instruction inCGM, the glucose levels recorded throughout the study were consistentwith the final HbA1C values.

FIG. 19 is a tabular illustration of three hypothetical subjects toevaluate and modify target continuously monitored glucose levels basedon HbA1C measurements, average 30 day CGM data, and percentage ofduration in hypoglycemic condition (<70 mg/dL) over the 30 day period inone aspect. As shown, patient 1 may be considered a “sensitive glycator”(see, e.g., FIG. 8) with a glycability ratio of 17. Also, it can be seenthat the rate of hypoglycemia is relatively high. Thus, a therapyrecommendation or compromise may include a target predicted HbA1C levelof 6.5% which, for the sensitive glycator, may translate to an averageCGM level of 113 mg/dL. Referring to FIG. 19, patient 2 profile issimilar to patient 1, but is not quite as sensitive a glycator, andthus, the CGM target may be at 118 mg/dL, with a predicted oranticipated HbA1C of 6.0% (which is considered to be still “in-target”).Patient 3 as shown, may be considered an “insensitive glycator” and hasa very low rate of hypoglycemia. Thus, despite having an in-target HbA1Cof 6.1%, the recommended therapy management may include a morecontrolled HbA1C level of 5.5% corresponding to an average CGM level of142 mg/dL.

For example, Patient A may begin at an HbA1C of 8.0%. He may beknowledgeable about food-insulin balancing and mealtime glucosecorrections, but still feels overwhelmed by mealtime decisions. Lookingat HbA1C and CGM data summary, Patient A's health care provider (HCP)sees that at meal times he has the following characteristics:

-   -   HbA1C=8.0    -   Starting meals in target 53% of the time    -   Staying in target for 40% of those    -   Moving into target 33% of the time when starting out of target

The HCP may recommend continuing to focus on starting meals in targetand staying there, and to maintain the rest of the therapy practices.Three months later, Patient A returns with these glucose metrics:

-   -   HbA1C=7.6    -   Starting meals in target 64% of the time    -   Staying in target for 53% of those    -   Moving into target 29% of the time when starting out of target

It can be seen that Patient A's HbA1C level is closer to target, andimproving in the areas that Patient A focused on for the prior 3 months.At this point, the HCP recommends that glucose corrections at mealtimesshould be the priority, while maintaining the rest of the therapydecisions. Patient A gets further training in mealtime corrections. Asthe months progress, Patient A improves mealtime glucose and has thefollowing glucose metrics:

-   -   HbA1C=6.9    -   Starting meals in target 65% of the time    -   Staying in target for 60% of those    -   Moving into target 59% of the time when starting out of target        It can be seen that Patient A's HbA1C is now in target, and        Patient A and the HCP decide to maintain the therapy practices        for the next few months.

Accordingly, embodiments of the present disclosure provide determinationof individualized HbA1C target levels based on mean glucose values aswell as other parameters such as the patient's prior HbA1C levels(determined based on a laboratory result or by other ways) to improveglycemic control. Furthermore, other metrics or parameters may befactored into the determination of the individualized HbA1C target levelsuch as, for example, conditions that may be relevant to the patient'shypoglycemic conditions including patient's age, hypoglycemiaunawareness, whether the patient is living alone or in assisted care, orwith others, history hypoglycemia, whether the patient is an insulinpump user, or is under insulin or other medication therapy, thepatient's activity levels and the like.

Additionally, other parameters may also include different or variableweighing functions to determine the mean glucose values, based on, forexample, the time of day, or time weighted measures, and the like.Furthermore, the determination of the individualized HbA1C target levelmay also include patient specific relationship between HbA1C and meanglucose values, including the rate of glycation, erythrocyte lifespan,among others. Also, embodiments may include weighing functions orparameters based on the patient's risk of high and low blood glucoselevels.

In accordance with the embodiments of the present disclosure, theindividualized HbA1C target level may be provided to the patient in realtime or retrospectively, and further, one or more underlying therapyrelated parameters may be provided to the patient or programmed in ananalyte monitoring system such as, for example, but not limited to, thereceiver unit 104 of the analyte monitoring system 100 (FIG. 1). In thismanner, therapy management settings, for example, on the receiver unit104 such as alarm threshold settings, projected alarm sensitivities,target glucose levels, modification to insulin basal level,recommendation of a bolus intake, and the like may be presented to thepatient or provided to the patient's healthcare provider to improve thepatient's therapy management.

The illustrations below provide some non-limiting examples ofdetermining an individual's glycemic targets based on the individualdifferences in glycability.

FIG. 20 illustrates routines for managing diabetic conditions based onHbA1C (also referred to as A1C) level and mean glucose data in oneaspect. Referring to FIG. 20, in one aspect, with the mean glucose data(CGM or SMBG) (2010) and laboratory determined HbA1C results (2020), alinear or nonlinear model (2040) may be applied to the glucose data(2010) and the HbA1C data (2020) in conjunction with the individualizedrelationship or correlation between the mean glucose data and the HbA1Cdata (2050). In one aspect, the individualized relationship orcorrelation (2050) may include, but is not limited to, the rate ofglycation, and/or the erythrocyte lifespan, for example, among others.

As shown in the Figure, based on the model (2040) applied in conjunctionwith the determined relationship between the mean glucose level andHbA1C level (2050), individualized HbA1C level may be determined eitherin real time, or retrospectively (2060). For example, using aretrospective data management system based on one or more dataprocessing algorithms or routines, for example, based on the CoPilot™system discussed above, determination of future or prediction of currentHbA1C level may be ascertained based, for example, on feedback onperformance over a predetermined time duration, such as 30 days, 45days, 60 days, 90 days, and so on. In a further aspect, theindividualized HbA1C level determination may be performed in real time,based on real time CGM data, with trend arrows or indicators on the CGMsystem reflecting a trend or glucose data rate of change over a 3 hour,12 hour, 24 hour, weekly, or monthly time period, or other suitable timeframe.

Referring back to FIG. 20, it can be seen that, in a further aspect, inaddition to the mean glucose data (2010) and the laboratory HbA1C level(2020), other input parameters (2030) may be provided to add robustnessto the system. Examples of other input parameters include, for example,but not limited to, glucose summary measures, weighting measures by timeof day, time weighted measures, and others. For example, in one aspect,glucose summary measures may include mean, median, standard deviation,interquartile range, median percentage, below, within or above certainthresholds. Also, in one aspect, the weighting measures by time of daymay include, for example, all day, specific time ranges, such as ±1 hourof a meal event, ±2 hours of a meal event, ±1 hour of an exercise event,±2 hours of an exercise event, etc., fasting time period, post-prandialtime period, post-breakfast time, post lunch time, post dinner time,pre-meal time, as well as post breakfast/lunch/dinner time relative totherapy administration and/or activity and the like. Additionally, thetime weighted measures may include, in one aspect, weighted measuresover a predetermined time period spaced, for example, differently, suchas by, 1, 5, 10, 15, or 30 day bins.

FIG. 21 illustrates routines for managing diabetic conditions based onHbA1C level and mean glucose data in another aspect. Compared to theillustration provided in FIG. 20, in embodiments shown in FIG. 21, thelinear/non-linear model may include a cost function (2130) which may beconfigured to weigh an individual's risk of high and/or low bloodglucose levels, in addition to accepting or factoring other inputparameters, such as, for example, HbA1C level targets (2110) and/orconditions associated or relevant to hypoglycemia (2120). In one aspect,conditions relevant to hypoglycemia provided as one or more inputparameters includes, for example, age, hypoglycemia unawareness, livingconditions (e.g., living alone), history of hypoglycemia, insulin pumpuser, frequency or manner of insulin or medication ingestion oradministration, activity level, among others.

Referring to FIG. 21, based on the input parameters provided to themodel function, a further output or results in addition to theindividualized HbA1C level determination or prediction, may includedevice or CGM system settings recommendation (2140) including, forexample, glucose level threshold alarm settings, projected alarmsensitivities associated with the monitored glucose values, alarmsettings or progression (e.g., increasing loudness/softness/strength invibration, etc.), glucose level target levels, and the like. Inaccordance with aspects of the present disclosure, the settingrecommendations (2140) or output may include treatment recommendationssuch as, for example, insulin/medication dosage information and/ortiming of administration of the same, information or recommendationrelated to exercise, meals, consultation with a healthcare provider, andthe like.

Experimental Study and Results #3

In a 90-day, 90-subject home use study of the FreeStyle Navigator®continuous glucose monitoring (CGM) system, participants were instructedon the built-in electronic logbook feature to indicate meals. While notrequired to record meals, the study resulted in 3,679 analyzablemealtime glucose profiles for 37 participants when at least 30 mealprofiles per subject were required. This data was retrospectivelyanalyzed to assess mealtime glucose relative to established glucosetargets, define per-subject summary mealtime glucose parameters, anddiscern summary parameters for subjects of different A1C levels.

Overall, the subjects had an average HbA1C level of 7.1% (SD=0.82%,min/max=5.6/9.2%), and were in target either before or after mealsaccording to ADA guidelines (90-130 mg/dL premeal, <180 mg/dL peakpostmeal) for 31% and 47% of meals, respectively. Only 20% of all mealswere in target both before and after meals. On a per subject basis, theresults indicate a correlation between HbA1C levels and mealtime glucosecontrol, and CGM system use illustrates trends and patterns around mealsthat differentiated those with higher and lower HbA1C values. Thosesubjects with the lowest HbA1C were able to most consistently achievethree patterns around meals: 1) start the meal in target, 2) stay intarget postmeal, and 3) correct to in-target levels postmeal if thepremeal value is out-of-target. Consistent use of the CGM systemcombined with health-care professional guidance for learning strategiesto manage mealtime glucose patterns has promise for improving therapychoices and glucose control.

In this manner, in one aspect, summary and assessment of glucose controlaround meals may be determined that can be effectively understood andacted upon by analyte monitoring system users and their health careproviders.

Mealtime therapy decisions are complex, as there are many interactingvariables or complications to arriving at a decision that will result ingood glucose level control. At each meal, there may be different factorssuch as: (1) time, amount and nutrient content to be consumed, (2)accuracy of the consumed nutrient content estimation (ie. “carbohydratecounting”); (3) current state of health (sickness, menses, stress, othermedications); (4) current amount of “insulin on board”; (5) recent prioractivity level (exercising vigorously or not); (6) current glucoselevel; (7) current glucose trend (“rate of change”, (mg/dL)/min); (8)maximum glucose after the meal; (9) minimum glucose after the meal; or(10) glucose at some timepoint after the meal (i.e. 2 hours).

In addition, there are individual factors to add to the complexity ofdetermining a suitable treatment option including, for example,time-of-day dependent insulin-to-carbohydrate ratio, and/or time-of-daydependent insulin sensitivity ratio.

As an individual and his or her health care provider (HCP) become moreinformed about the value and variation of these parameters, HbA1C level,monitored CGM level and meal times can be used to guide therapymodification and training choices. These factors may be related to CGMdata and summarized for different HbA1C levels to guide therapyadjustments and training.

FIG. 22 is a flowchart illustrating a therapy guidance routine based inpart on the HbA1C level in one aspect. It can be seen from FIG. 22, thatthe HbA1C level (2210), whether it is in target or out of target is asignificant factor in determining or guiding the therapy guidanceroutine, to determine or prompt the patient to decide whetherimprovement in HbA1C level (within target range) is desired (2220),and/or to determine whether the start meal in target range is greaterthan approximately 50% (2230) as illustrated in the figure. Therapyguidance may also be determined based on such factors as if the HbA1Clevel moved into the target range (2221) or stayed in the target range(2231).

In aspects of the present disclosure, nonlimiting recommendations basedon the routine set forth above (2222, 2223, 2232, 2233, 2234) include,for example, (1) improve understanding and enable improvement ofestimates of meal amount and nutrient content, (2) improve understandingand enable adjustment of insulin dose needs, (3) improve understandingand enable adjustment of insulin-to-carbohydrate ratio, (4) improveunderstanding and enable adjustment of insulin sensitivity ratio, (5)improve understanding of effect of meal choices on glucose control, (6)improve understanding of effect of exercise choices on glucose control,(7) improve understanding of effect of states of health (sickness,menses, stress, other medications) on glucose control, (8) identifypatients in need of additional training in different aspects oftherapy-decision making, (9) balancing food and insulin, (10) correctingglucose level with insulin, and/or (11) balancing food intake andcorrecting glucose level with insulin.

Therapy guidelines are followed for a predetermined time period, such as3 months (2240), before a new HbA1C level is measured (2250). Based onthe new measured HbA1C level, therapy management and guidance may bealtered accordingly.

In this manner, in one aspect, summary and assessment of glucose controlaround meal events may be determined that can be effectively understoodand acted upon by analyte monitoring system users and their health careproviders.

A method in one embodiment, may comprise receiving mean glucose valueinformation of a patient based on a predetermined time period, receivinga current HbA1C level of the patient, determining whether the currentHbA1C level of the patient received is within a predefined target range,and if it is determined that the current HbA1C level is not within thepredefined target range, determining one or more corrective action foroutput to the patient, and if it is determined that the current HbA1Clevel is within the predetermined target range, analyzing the glucosedirectional change information around one or more meal events, anddetermining a modification to a current therapy profile.

An apparatus in one embodiment may comprise, a communication interface,one or more processors operatively coupled to the communicationinterface, and a memory for storing instructions which, when executed bythe one or more processors, causes the one or more processors to receivemean glucose value information of a patient based on a predeterminedtime period, receive a current HbA1C level of the patient, determinewhether the current HbA1C level of the patient received is within apredefined target range, if it is determined that the current HbA1Clevel is not within the predefined target range, determine one or morecorrective action for output to the patient, and if it is determinedthat the current HbA1C level is within the predetermined target range,to analyze the glucose directional change information around one or moremeal events, and determine a modification to a current therapy profile.

In one embodiment, a method may include receiving mean glucose valueinformation of a patient based on a predetermined time period, receivinga current HbA1C level of the patient and a target HbA1C level of thepatient, determining a correlation between the received mean glucosevalue information and the retrieved current and target HbA1C levels,updating the target HbA1C level based on the determined correlation, anddetermining one or more parameters associated with the physiologicalcondition of the patient based on the updated target HbA1C level.

In one aspect, receiving mean glucose value information may includereceiving monitored glucose level information over the predeterminedtime period, and applying a weighting function to the received monitoredglucose level information.

The weighting function may be based on a time of day informationassociated with the received monitored glucose level information.

The weighting function may be based on a time period associated with thereceived monitored glucose level information.

In another aspect, updating the target HbA1C level may include receivingone or more patient specific parameters, and applying the received oneor more patient specific parameters to the determined correlationbetween the received mean glucose value information and the receivedcurrent HbA1C level.

The one or more patient specific parameters may include an age of thepatient, a history of hypoglycemia, an activity level of the patient, amedication intake information of the patient, or a risk associated withhigh or low blood glucose levels of the patient.

The determined correlation between the received mean glucose valueinformation and the received current HbA1C level may include a rate ofglycation of the patient.

The predetermined time period may include one of approximately 30 days,approximately 45 days, or approximately 90 days.

In another aspect, determining one or more parameters associated withthe physiological condition of the patient may include one or more ofproviding modification to current alarm settings, providing modificationto current target threshold settings related to the monitored analytelevels, or providing a modification to a medication intake level.

Furthermore, the method may include storing one or more of the meanglucose value information, the received current or target HbA1C level,the determined correlation between the received mean glucose valueinformation and the current HbA1C level, and the updated target HbA1Clevel.

In another embodiment, an apparatus may include a communicationinterface, one or more processors operatively coupled to thecommunication interface, and a memory for storing instructions which,when executed by the one or more processors, may cause the one or moreprocessors to receive mean glucose value information of a patient basedon a predetermined time period, receive a current HbA1C level of thepatient and a target HbA1C level of the patient, determine a correlationbetween the received mean glucose value information and the retrievedcurrent and target HbA1C levels, update the target HbA1C level based onthe determined correlation, and to determine one or more parametersassociated with the physiological condition of the patient based on theupdated target HbA1C level.

In one aspect, the memory for storing instructions which, when executedby the one or more processors, may cause the one or more processors toreceive monitored glucose level information over the predetermined timeperiod, to apply a weighting function to the received monitored glucoselevel information.

The weighting function may be based on a time of day informationassociated with the received monitored glucose level information.

The weighting function may be based on a time period associated with thereceived monitored glucose level information.

In another aspect, the memory for storing instructions which, whenexecuted by the one or more processors, may cause the one or moreprocessors to receive one or more patient specific parameters, and toapply the received one or more patient specific parameters to thedetermined correlation between the received mean glucose valueinformation and the received current HbA1C level.

The one or more patient specific parameters may include an age of thepatient, a history of hypoglycemia, an activity level of the patient, amedication intake information of the patient, or a risk associated withhigh or low blood glucose levels of the patient.

The determined correlation between the received mean glucose valueinformation and the received current HbA1C level may include a rate ofglycation of the patient.

The predetermined time period may include one of approximately 30 days,approximately 45 days, or approximately 90 days.

In another aspect, the memory for storing instructions which, whenexecuted by the one or more processors, may cause the one or moreprocessors to provide a modification to current alarm settings, providemodification to current target threshold settings related to themonitored analyte levels, or provide modification to a medication intakelevel.

In yet another aspect, the memory for storing instructions which, whenexecuted by the one or more processors, may cause the one or moreprocessors to store one or more of the mean glucose value information,the received current or target HbA1C level, the determined correlationbetween the received mean glucose value information and the HbA1C level,and the updated target HbA1C level.

The various processes described above including the processes performedby the processor 204 (FIG. 2) in the software application executionenvironment in the analyte monitoring system (FIG. 1) as well as anyother suitable or similar processing units embodied in the processing &storage unit 307 (FIG. 3) of the primary/secondary receiver unit104/106, and/or the data processing terminal/infusion section 105,including the processes and routines described hereinabove, may beembodied as computer programs developed using an object orientedlanguage that allows the modeling of complex systems with modularobjects to create abstractions that are representative of real world,physical objects and their interrelationships. The software required tocarry out the inventive process, which may be stored in a memory orstorage unit (or similar storage devices) in the one or more componentsof the system 100 and executed by the processor, may be developed by aperson of ordinary skill in the art and may include one or more computerprogram products.

What is claimed is:
 1. A computer-implemented method to determine one or more parameters associated with a physiological condition of a person, comprising: determining a correlation between a mean glucose value information and a current and target HbA1C levels of a person; determining a rate of glycation of the person based at least in part on the determined correlation between the mean glucose value information and the current HbA1C level; applying one or more person specific parameters to the determined correlation between the mean glucose value information and the current HbA1C level; updating the target HbA1C level based on the determined rate of glycation and the application of received one or more person specific parameters to the determined correlation; and one or more of modifying a current alarm setting, modifying a current target threshold setting related to monitored analyte levels, or modifying a medication intake level based on the updated target HbA1C level.
 2. The method of claim 1, wherein the mean glucose value information determination includes monitoring glucose level information over a predetermined time period applying a weighting function to received monitored glucose level information.
 3. The method of claim 2, wherein the weighting function is based on a time of day information associated with the monitored glucose level information.
 4. The method of claim 2, wherein the weighting function is based on a time period associated with the monitored glucose level information.
 5. The method of claim 1, wherein the one or more person specific parameters includes an age of the person, a history of hypoglycemia, an activity level of the person, a medication intake information of the person, or a risk associated with high or low blood glucose levels of the person.
 6. The method of claim 2, wherein the predetermined time period includes one of approximately 30 days, approximately 45 days, or approximately 90 days.
 7. The method of claim 1, including storing one or more of the mean glucose value information, the current or target HbA1C level, the determined correlation between the mean glucose value information and the current HbA1C level, and the updated target HbA1C level.
 8. The method of claim 1, wherein the rate of glycation is a ratio of the mean glucose value information with respect to the current HbA1C level.
 9. The method of claim 1, wherein the mean glucose value information is obtained using a glucose sensor that comprises a plurality of electrodes including a working electrode, wherein the working electrode comprises a glucose-responsive enzyme and a mediator, wherein at least one of the glucose-responsive enzyme and the mediator is chemically bonded to a polymer disposed on the working electrode, and wherein at least one of the glucose-responsive enzyme and the mediator is crosslinked with the polymer.
 10. The method of claim 1, wherein the glucose sensor is factory calibrated.
 11. An apparatus, comprising: a communication interface; one or more processors operatively coupled to the communication interface; and a memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to determine a correlation between mean glucose value information and current and target HbA1C levels, determine a rate of glycation of a person based at least in part on the determined correlation between the mean glucose value information and the current HbA1C level, apply one or more person specific parameters to the determined correlation between the mean glucose value information and the current HbA1C level, update the target HbA1C level based on the determined rate of glycation of the person and the application of received one or more person specific parameters to the determined correlation, and one or more of modify a current alarm setting, modify a current target threshold setting related to monitored analyte levels, or modify a medication intake level based on the updated target HbA1C level.
 12. The apparatus of claim 11, wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to receive monitored glucose level information over a predetermined time period, and to apply a weighting function to the received monitored glucose level information to determine the mean glucose value information.
 13. The apparatus of claim 12, wherein the weighting function is based on a time of day information associated with the monitored glucose level information.
 14. The apparatus of claim 12, wherein the weighting function is based on a time period associated with the monitored glucose level information.
 15. The apparatus of claim 11, wherein the one or more person specific parameters includes an age of the person, a history of hypoglycemia, an activity level of the person, a medication intake information of the person, or a risk associated with high or low blood glucose levels of the person.
 16. The apparatus of claim 12, wherein the predetermined time period includes one of approximately 30 days, approximately 45 days, or approximately 90 days.
 17. The apparatus of claim 11, wherein the memory for storing instructions which, when executed by the one or more processors, causes the one or more processors to store one or more of the mean glucose value information, the current or target HbA1C level, the determined correlation between the mean glucose value information and the current HbA1C level, and the updated target HbA1C level.
 18. The apparatus of claim 11, wherein the rate of glycation is a ratio of the mean glucose value information with respect to the current HbA1C level.
 19. The apparatus of claim 11, wherein the mean glucose value information is obtained using a glucose sensor that comprises a plurality of electrodes including a working electrode, wherein the working electrode comprises a glucose-responsive enzyme and a mediator, wherein at least one of the glucose-responsive enzyme and the mediator is chemically bonded to a polymer disposed on the working electrode, and wherein at least one of the glucose-responsive enzyme and the mediator is crosslinked with the polymer.
 20. The apparatus of claim 19, wherein the glucose sensor is factory calibrated. 